This study was performed to investigate the ecosystem service patterns in relation to climate change acceleration utilizing big data analysis. This study aimed to use big data analysis as one of the network of views to identify convergent thinking in two fields: climate change and ecosystem service. The keywords were analysed to ascertain if there were any differences in the perceiving problems, policy direction, climate change implications, and regional differences. In addition, we examined the research keywords of each continent, the centre of ecosystem service research, and the topics to be referred to in domestic research. The results of the analysis are as follows: First, the keyword centrality of climate change is similar to the detailed indicators of The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) regulations, content, and non-material ecosystem services. Second, the cross-analysis of terms in two journals showed a difference in value-oriented point; the Ecosystem Service Journal identified green infrastructure as having economic value, whereas the Climate Change Journal perceives water, forest, carbon, and biodiversity as management topics. The Climate Change Journal, but not the former, focuses on future predictions. Third, the analysis of the research topics according to continents showed that water and soil are closely related to the economy, and thus, play an important role in policy formulation. This disparity is due to differences in each continent's environmental characteristics, as well as economic and policy issues. This fact can be used to refer to the direction of research on ecosystem services in Korea. Consistent with the recent trend of expanding research regarding the impacts of climate change, it is necessary to study strategies to scientifically predict and respond to the negative effects of climate change.

Syntaxonomy and Synecology on Quercus variabilis forests in Daecheong-dam basin was carried out using the methods of Braun-Blanquet phytosociology. 6 syntaxa classified as species compositions described were Quercus variabilis community, Platycarya strobilacea-Quercus variabilis community(typicum subcommunity, dictamnus dasycarpus subcommunity), Quercetum variabili-serratae, Zelkova serrata-Quercus variabilis community and Dendranthema boreale-Quercus variabilis community. All syntaxa were shown habitat environmental conditions including steep inclination of more than 30°, high rock exposure rate of more than 50% and South-facing slope. These communities excepting Dendranthema boreale-Quercus variabilis community classified as natural vegetation were identified as low emergence rate of annual plants and species compositions composed native species, so it was confirmed that relatively natural succession were proceeding well. Quercetum variabili-serratae and Dendranthema boreale-Quercus variabilis community distributed forested hillslope of open water edge were representative Quercus variabilis syntaxa in Daecheong-dam basin.

In Suwon, the overall hygiene of the city is threatened by the emergence of the rook(Corvus fugilegus) in the city. Rooks began to appear in November of 2016 and has continued to appear from November to March every year. In order to eradicate or to prepare an alternative habitat for rooks, this study aimed to identify the preferred habitat and specific environmental variables. Therefore, in this work, we aim to understand the predicted distribution of rooks in Suwon City with citizen science and through MaxENT, the most widely utilized habitat modeling using citizen science to analyze the preferred habitat of harmful tides appearing in urban areas. In this study, seven environmental variables were chosen: biotope group complex, building floor, vegetation, euclidean distance from farmland, euclidean distance from streetlamp, and euclidean distance from pole and DEM. Among the estimated models, after the time period of sunrise (08:00~18:00) the contribution percentage were as following: euclidean distance from arable land(39.2%), DEM(25.5%), euclidean distance from streetlamp(22.3%), euclidean distance from pole(7.1%), biotope group complex(4.9%), building floor(1%), vegetation(0%). In the time period after sunset(18:00~08:00) the contribution percentage were as following: biotope group complex(437.4%), euclidean distance from pole(26.8%), DEM(13.4%), euclidean distance from streetlamp(11.8%), euclidean distance from farmland(7.9%), building floor(1.4%), vegetation(1.3%).

Although the Environmental Impact Assessment (EIA) is continuously being advanced, the number of environmental disputes regarding it is still on the rise. In order to supplement this, it is necessary to analyze the accumulated complaint cases. In this study, through the analysis of complaint cases, it is possible to identify matters that need to be improved in the existing EIA stages as well as various damages and conflicts that were not previously considered or predicted. In the process, we dervied 'complaint-based data demands' that should be additionally examined to improve the EIA. To this end, a total of 348 news articles were collected by searching with combinations of 'environmental impact assessment' and a keyword for each of the six assessment groups. As a result of analysis of collected data, a total of 54 complaint-based data demands were suggested. Among those were 15 items including 'impact of changes in seawater flow on water quality' in the category of water environment; 13 items including 'area of green buffer zone' in atmospheric environment; 10 items including 'impact of soundproof wall on wind corridor' in living environment; 8 items including 'expected number of users' in socioeconomic environment, 4 items including 'feasibility assessment of development site in terms of environmental and ecological aspects' in natural ecological environment; and 4 items including 'prediction of sediment runoff and damaged areas according to the increase in intensity and frequency of torrential rain' in land environment. In future research, more systematic complaint collection and analysis as well as specific provision methods regarding stages, subjects, and forms of use should be sought to apply the derived data demands in the actual EIA process. It is expected that this study can serve to advance the prediction and assessment of EIA in the future and to minimize environmental impact as well as social conflict in advance.

Provisioning service, which is one of the ecosystem service functions, means goods and services such as food and fuel that people get from ecosystem. Provisioning functions are closely related to the primary industry, a sector of economy. Excessive demand and use of human society can cause trade-offs among regulation, cultural, and supporting services. Therefore, it is important to perform evaluation ecosystem services periodically and to monitor the time series fluctuations to identify the impact of provisioning services on other ecosystem services (trade-off) and to maintain sustainable provisioning service. When it comes to the precise assessment of provisioning service, it is necessary to get the statistical data and standardize indicators and methods. In this study, indicators and methods, which are applicable to the spatial level of national-local-protected areas, were derived through literature analysis and expert survey. The result of this study implies that provisioning services measurement by spatial level improve the efficiency of the establishment of environmental conservation plans by whose purpose.

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.

Abnormal climate caused by climate change causes enormous social and economic damage. And such damage and its impact may vary depending on the location and regional characteristics of the region and the social and economic conditions of local residents. Therefore, it is necessary to continuously monitor whether there are indicators that are weaker than other regions among the detailed indicators that constitute the risk, exposure and vulnerability of climate change risk. In this study, the concept of climate change risk was used for heatwave to determine regional inequality of climate change risk. In other words, it was judged that inequality in climate change risk occurred in regions with high risk but high exposure and low vulnerability compared to other regions. As a result of the analysis, it was found that 13 local governments in Korea experienced regional inequality in climate change risk. In order to resolve regional inequality in climate change risks, the current status of regional inequality in climate change should be checked based on the analysis proposed in this study, there is a need for an evaluation and monitoring system that can provide appropriate feedback on areas where inequality has occurred. This continuous evaluation and monitoring-based feedback system is expected to be of great help in resolving regional inequality in climate change risks.

Thermal environment of city is getting worse due to severe urban heat island caused by climate change and urbanization. Green roof improves the urban thermal environment and save the cooling energy in buildings. This study presented a green roof combined with a storage system that stores rainwater and supplies water through a wick and evaluated the temperature reduction effect as surface temperature and amount of evapotranspiration. For about a week, the surface temperature using a infrared thermal imager and the evapotranspiration by recording change of module weight were measured at intervals of 30 minutes from sunrise to sunset. The results show that the mean surface temperature of the green roof was 15.4 degrees lower than that of the non-green roof from 12:00 P.M. to 14:00 P.M. There was no significant difference between mean surface temperature of green roof with and without storage system immediately after rain, but more than a week after rain, there was a difference with average of 2.49 degrees and maximum of 4.72 degrees. The difference in daily amount of evapotranspiration was measured to be 1.66 times on average. As drought stress increased over time, the difference in daily amount of evapotranspiration and surface temperature between with/without storage system increased simultaneously. The results of the study show a more excellent cooling effect of green roof combined with the rainwater storage system.

Green facade has a significant impact on building's energy performance by controlling the absorption of solar radiation and improving outdoor thermal comfort through shading and evapotranspiration. In particular, since high-density building does not enough green space, green facade, and rooftop greening using artificial ground plants are highly utilized. However, the level of cooling effect according to plant traits and irrigation control is different. Therefore, in this study, the cooling effect analyzed for a total of 4 cases by controlling the irrigation condition based on hedera and spurge. Although hedera under sufficient water had the highest cooling effect(-2℃~-4℃), had the lowest cooling effect under non-irrigation(+1.1℃~+4.4℃). In addition, hedera under sufficient water had cooling effect than hedera under non-irrigation(-1℃~-8.1℃) and in the case of spurge, it had cooling effect(-0.3℃~-7.8℃) more than non-irrigation. As a result of measuring the amount of transpiration according to the light intensity (PAR) and carbon dioxide concentration conditions, transpiration of hedera was higher than the spurge (respectively 0.63204mmolm-2s-1, 0.674367mmolm-2s-1). The difference in the cooling effect of the green facade under irrigation condition was significant. But the potential cooling effect of green facade according to plants species was different. Therefore, in order to maximize and continuously provide the cooling effect of green facade in urban areas, it is necessary to consider the characteristics of plants and the control of water supply through the irrigation system.

This study aims to estimate location of land mammals habitat by analyzing spatial data and investigate how to apply environmental DNA monitoring methodology to lotic system in Yangpyeong-gun, Gyeonggi-do. Environmental DNA sampling points are selected through spatial analysis with QGIS open source program by overlaying Kernel density of wild boar(Sus scrofa), elevation, slope and land-cover map, and 81 samples are collected. After 240 mL of water was filtered in each sample, metabarcoding technique using MiMammal universal primer was applied in order to get a whole list of mammal species whose DNA particles contained in filtered water. 8 and 22 samples showed DNA of wild boar and water deer, respectively. DNA of raccoon dog, Eurasian otter, and Siberian weasel are also detected through metabarcoding analysis. This study is valuable that conducted in outdoor lotic system. The study suggests a new wildlife monitoring methodology integrating overlayed geographic data and environmental DNA.